
import pandas as pd


import warnings
warnings.filterwarnings('ignore')


crp_rcmnd = pd.read_csv("Crop_recommendation.csv")


crp_rcmnd


crp_rcmnd.dtypes

N                int64
P                int64
K                int64
temperature    float64
humidity       float64
ph             float64
rainfall       float64
label           object
dtype: object


crp_rcmnd.columns

Index(['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall', 'label'], dtype='object')


crp_rcmnd.index

RangeIndex(start=0, stop=2200, step=1)


crp_rcmnd.describe()


crp_rcmnd.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2200 entries, 0 to 2199
Data columns (total 8 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   N            2200 non-null   int64  
 1   P            2200 non-null   int64  
 2   K            2200 non-null   int64  
 3   temperature  2200 non-null   float64
 4   humidity     2200 non-null   float64
 5   ph           2200 non-null   float64
 6   rainfall     2200 non-null   float64
 7   label        2200 non-null   object 
dtypes: float64(4), int64(3), object(1)
memory usage: 137.6+ KB


crp_rcmnd.mean()

N               50.551818
P               53.362727
K               48.149091
temperature     25.616244
humidity        71.481779
ph               6.469480
rainfall       103.463655
dtype: float64


crp_rcmnd.head()


crp_rcmnd.tail()


crp_rcmnd.loc[175]

N                     77
P                     52
K                     17
temperature    24.863749
humidity       65.742005
ph                5.7148
rainfall       75.822705
label              maize
Name: 175, dtype: object


crp_rcmnd.label

0         rice
1         rice
2         rice
3         rice
4         rice
         ...  
2195    coffee
2196    coffee
2197    coffee
2198    coffee
2199    coffee
Name: label, Length: 2200, dtype: object


crp_rcmnd[crp_rcmnd.label == "mango"]


crp_rcmnd[crp_rcmnd.label == "watermelon"]


crp_rcmnd[crp_rcmnd.ph <= 4.0]


pd.crosstab(crp_rcmnd.N ,crp_rcmnd.label)


crp_rcmnd.groupby(["label"]).mean()


crp_rcmnd.isnull()


crp_rcmnd.isnull().values.any()

False


crp_rcmnd


crp_rcmnd.sample(frac = 1).head(10)


crp_rcmnd.shape

(2200, 8)


crp_rcmnd.duplicated().sum()

0


crp_rcmnd.corr()


import seaborn as sns
import matplotlib as plt
import matplotlib.pyplot as plt
import numpy as np


corr = crp_rcmnd.corr()
sns.heatmap(corr, annot=True, cbar=True, cmap= 'coolwarm')

<AxesSubplot:>


crp_rcmnd = crp_rcmnd.drop("rainfall", axis =1)


crp_rcmnd


crp_rcmnd["label"].unique()

array(['rice', 'maize', 'chickpea', 'kidneybeans', 'pigeonpeas',
       'mothbeans', 'mungbean', 'blackgram', 'lentil', 'pomegranate',
       'banana', 'mango', 'grapes', 'watermelon', 'muskmelon', 'apple',
       'orange', 'papaya', 'coconut', 'cotton', 'jute', 'coffee'],
      dtype=object)


crp_rcmnd["label"].value_counts()

rice           100
maize          100
jute           100
cotton         100
coconut        100
papaya         100
orange         100
apple          100
muskmelon      100
watermelon     100
grapes         100
mango          100
banana         100
pomegranate    100
lentil         100
blackgram      100
mungbean       100
mothbeans      100
pigeonpeas     100
kidneybeans    100
chickpea       100
coffee         100
Name: label, dtype: int64


crp_rcmnd.plot(x = "label", y ="N" , kind= "scatter", figsize=(28,5))

<AxesSubplot:xlabel='label', ylabel='N'>


crp_rcmnd["P"].plot.hist()

<AxesSubplot:ylabel='Frequency'>


orange = crp_rcmnd[crp_rcmnd["label"] == "orange"]


len(orange)

100


orange.head()


crp_rcmnd_mean = pd.pivot_table(crp_rcmnd,index=['label'],aggfunc='mean')


from plotly.subplots import make_subplots
import plotly.graph_objects as go
import random
number_of_colors = 16

col_arr = ["#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)])
             for i in range(number_of_colors)]


phos_summary = crp_rcmnd_mean.sort_values(by='P',ascending=False)

fig = make_subplots(rows=1, cols=2)

top_10 = { 'y': phos_summary['P'][0:10].sort_values().index,
           'x': phos_summary['P'][0:10].sort_values() 
         }

last_10 = { 'y': phos_summary['P'][-10:].sort_values().index,
           'x': phos_summary['P'][-10:].sort_values() 
         }

fig.add_trace(
                go.Bar( top_10,
                       marker_color= random.choice(col_arr),
                       name = 'Most Phosporus Required',
                       orientation = 'h',
                       text = top_10['x']
                      ),
                row=1, col=1
             )

fig.add_trace(
                go.Bar( last_10,
                       marker_color= random.choice(col_arr),
                       name = 'Least Phosporus Required',
                       orientation = 'h',
                       text = last_10['x']
                      ),
                row=1, col=2
             )

fig.update_traces(texttemplate='%{text}',textposition='inside')
fig.update_layout(title='Phosporus',font_size=12)
fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)
fig.show()


fig = go.Figure()

fig.add_trace( go.Bar( x = crp_rcmnd_mean.index,
                       y = crp_rcmnd_mean['N'],
                      marker_color = 'blue',
                      name = 'Nitrogen'
                     )
             )

fig.add_trace( go.Bar( x = crp_rcmnd_mean.index,
                       y = crp_rcmnd_mean['P'],
                      marker_color = 'light blue',
                      name = 'Phosphorous'
                     )
             )

fig.add_trace( go.Bar( x = crp_rcmnd_mean.index,
                       y = crp_rcmnd_mean['K'],
                      marker_color = 'green',
                      name = 'Potassium'
                     )
             )
fig.update_layout(title='Comparison between N, P, k')


features = crp_rcmnd[['N', 'P','K','temperature', 'humidity', 'ph']]
target = crp_rcmnd['label']
labels = crp_rcmnd['label']


acc = []
model = []


from sklearn.model_selection import train_test_split
Xtrain, Xtest, Ytrain, Ytest = train_test_split(features,target,test_size = 0.2,random_state =2)


from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score

DecisionTree = DecisionTreeClassifier(criterion="entropy",random_state=2,max_depth=11)

DecisionTree.fit(Xtrain,Ytrain)

predicted_values = DecisionTree.predict(Xtest)
x = metrics.accuracy_score(Ytest, predicted_values)
acc.append(x)
model.append('Decision Tree')
print("DecisionTrees's Accuracy is: ", x*100)

print(classification_report(Ytest,predicted_values))

DecisionTrees's Accuracy is:  95.9090909090909
              precision    recall  f1-score   support

       apple       1.00      1.00      1.00        13
      banana       1.00      1.00      1.00        17
   blackgram       0.73      1.00      0.84        16
    chickpea       1.00      1.00      1.00        21
     coconut       1.00      1.00      1.00        21
      coffee       1.00      1.00      1.00        22
      cotton       1.00      1.00      1.00        20
      grapes       1.00      1.00      1.00        18
        jute       0.81      0.89      0.85        28
 kidneybeans       1.00      1.00      1.00        14
      lentil       1.00      0.78      0.88        23
       maize       1.00      1.00      1.00        21
       mango       1.00      1.00      1.00        26
   mothbeans       0.94      0.84      0.89        19
    mungbean       1.00      1.00      1.00        24
   muskmelon       1.00      1.00      1.00        23
      orange       1.00      1.00      1.00        29
      papaya       1.00      0.95      0.97        19
  pigeonpeas       0.89      0.94      0.92        18
 pomegranate       1.00      1.00      1.00        17
        rice       0.79      0.69      0.73        16
  watermelon       1.00      1.00      1.00        15

    accuracy                           0.96       440
   macro avg       0.96      0.96      0.96       440
weighted avg       0.96      0.96      0.96       440


from sklearn.model_selection import cross_val_score


# Cross validation score (Decision Tree)
score = cross_val_score(DecisionTree, features, target,cv=5)


score

array([0.95227273, 0.95454545, 0.95681818, 0.94772727, 0.95227273])


import pickle
# Dump the trained Naive Bayes classifier with Pickle
DT_pkl_filename = 'DecisionTree.pkl'
# Open the file to save as pkl file
DT_Model_pkl = open(DT_pkl_filename, 'wb')
pickle.dump(DecisionTree, DT_Model_pkl)
# Close the pickle instances
DT_Model_pkl.close()


from sklearn.metrics import confusion_matrix
cm = confusion_matrix(Ytest, predicted_values)

plt.figure(figsize=(15,15))
sns.heatmap(cm, annot=True, fmt=".0f", linewidths=.5, square = True, cmap = 'Blues');
plt.ylabel('Actual label');
plt.xlabel('Predicted label');
all_sample_title = 'Confusion Matrix - score:'+str(accuracy_score(Ytest,predicted_values))
plt.title(all_sample_title, size = 15);
plt.show()


data = pd.DataFrame([[83, 45, 60, 28, 70.3, 7.0]], columns=['N', 'P','K','temperature', 'humidity', 'ph'])
prediction = DecisionTree.predict(data)
print(prediction)

['jute']


from sklearn.svm import SVC

SVM = SVC(gamma='auto')

SVM.fit(Xtrain,Ytrain)

predicted_values = SVM.predict(Xtest)

x = metrics.accuracy_score(Ytest, predicted_values)
acc.append(x)
model.append('SVM')
print("SVM's Accuracy is: ", x)

print(classification_report(Ytest,predicted_values))

SVM's Accuracy is:  0.31363636363636366
              precision    recall  f1-score   support

       apple       1.00      0.62      0.76        13
      banana       1.00      0.47      0.64        17
   blackgram       0.67      0.38      0.48        16
    chickpea       1.00      0.24      0.38        21
     coconut       1.00      0.33      0.50        21
      coffee       1.00      0.23      0.37        22
      cotton       1.00      0.50      0.67        20
      grapes       1.00      0.22      0.36        18
        jute       1.00      0.25      0.40        28
 kidneybeans       0.05      1.00      0.09        14
      lentil       0.75      0.13      0.22        23
       maize       1.00      0.10      0.17        21
       mango       1.00      0.15      0.27        26
   mothbeans       1.00      0.21      0.35        19
    mungbean       1.00      0.21      0.34        24
   muskmelon       0.92      0.52      0.67        23
      orange       1.00      0.10      0.19        29
      papaya       1.00      0.37      0.54        19
  pigeonpeas       0.33      0.11      0.17        18
 pomegranate       0.91      0.59      0.71        17
        rice       0.67      0.50      0.57        16
  watermelon       0.80      0.27      0.40        15

    accuracy                           0.31       440
   macro avg       0.87      0.34      0.42       440
weighted avg       0.89      0.31      0.41       440


# Cross validation score (SVM)
score = cross_val_score(SVM,features,target,cv=5)
score

array([0.48863636, 0.44772727, 0.44090909, 0.475     , 0.475     ])


data = pd.DataFrame([[83, 45, 60, 28, 70.3, 7.0]], columns=['N', 'P','K','temperature', 'humidity', 'ph'])
prediction = DecisionTree.predict(data)
print(prediction)

['jute']


from sklearn.ensemble import RandomForestClassifier

RF = RandomForestClassifier(n_estimators=30, max_depth=14, random_state=0)
RF.fit(Xtrain,Ytrain)

predicted_values = RF.predict(Xtest)

x = metrics.accuracy_score(Ytest, predicted_values)
acc.append(x)
model.append('RF')
print("RF's Accuracy is: ", x)

print(classification_report(Ytest,predicted_values))

RF's Accuracy is:  0.975
              precision    recall  f1-score   support

       apple       1.00      1.00      1.00        13
      banana       1.00      1.00      1.00        17
   blackgram       0.88      0.88      0.88        16
    chickpea       1.00      1.00      1.00        21
     coconut       1.00      1.00      1.00        21
      coffee       1.00      1.00      1.00        22
      cotton       1.00      1.00      1.00        20
      grapes       1.00      1.00      1.00        18
        jute       0.96      0.89      0.93        28
 kidneybeans       1.00      1.00      1.00        14
      lentil       0.91      0.91      0.91        23
       maize       1.00      1.00      1.00        21
       mango       1.00      1.00      1.00        26
   mothbeans       0.94      0.89      0.92        19
    mungbean       1.00      1.00      1.00        24
   muskmelon       1.00      1.00      1.00        23
      orange       1.00      1.00      1.00        29
      papaya       1.00      1.00      1.00        19
  pigeonpeas       0.89      0.94      0.92        18
 pomegranate       1.00      1.00      1.00        17
        rice       0.83      0.94      0.88        16
  watermelon       1.00      1.00      1.00        15

    accuracy                           0.97       440
   macro avg       0.97      0.98      0.97       440
weighted avg       0.98      0.97      0.98       440


# Cross validation score (Random Forest)
score = cross_val_score(RF,features,target,cv=5)
score

array([0.97272727, 0.96136364, 0.97045455, 0.96136364, 0.97045455])


# Dump the trained Naive Bayes classifier with Pickle
RF_pkl_filename = 'RandomForest.pkl'
# Open the file to save as pkl file
RF_Model_pkl = open(RF_pkl_filename, 'wb')
pickle.dump(RF, RF_Model_pkl)
# Close the pickle instances
RF_Model_pkl.close()


from sklearn.metrics import confusion_matrix
cm = confusion_matrix(Ytest, predicted_values)

plt.figure(figsize=(15,15))
sns.heatmap(cm, annot=True, fmt=".0f", linewidths=.5, square = True, cmap = 'Blues');
plt.ylabel('Actual label');
plt.xlabel('Predicted label');
all_sample_title = 'Confusion Matrix - score:'+str(accuracy_score(Ytest,predicted_values))
plt.title(all_sample_title, size = 15);
plt.show()


data = pd.DataFrame([[83, 45, 60, 28, 70.3, 7.0]], columns=['N', 'P','K','temperature', 'humidity', 'ph'])
prediction = RF.predict(data)
print(prediction)

['jute']


from sklearn.neighbors import KNeighborsClassifier


knn = KNeighborsClassifier(n_neighbors= 15, algorithm = 'kd_tree', metric = 'manhattan')
knn.fit(Xtrain,Ytrain)
predicted_values = knn.predict(Xtest)

x = metrics.accuracy_score(Ytest, predicted_values)
acc.append(x)
model.append('KNN')
print("KNN's Accuracy is: ", x)

print(classification_report(Ytest,predicted_values))

KNN's Accuracy is:  0.9318181818181818
              precision    recall  f1-score   support

       apple       1.00      1.00      1.00        13
      banana       1.00      1.00      1.00        17
   blackgram       0.64      0.88      0.74        16
    chickpea       1.00      1.00      1.00        21
     coconut       1.00      1.00      1.00        21
      coffee       1.00      1.00      1.00        22
      cotton       0.95      1.00      0.98        20
      grapes       1.00      1.00      1.00        18
        jute       1.00      0.54      0.70        28
 kidneybeans       1.00      1.00      1.00        14
      lentil       0.72      0.78      0.75        23
       maize       1.00      0.95      0.98        21
       mango       1.00      1.00      1.00        26
   mothbeans       0.94      0.89      0.92        19
    mungbean       1.00      1.00      1.00        24
   muskmelon       1.00      1.00      1.00        23
      orange       1.00      1.00      1.00        29
      papaya       1.00      1.00      1.00        19
  pigeonpeas       1.00      0.61      0.76        18
 pomegranate       1.00      1.00      1.00        17
        rice       0.55      1.00      0.71        16
  watermelon       1.00      1.00      1.00        15

    accuracy                           0.93       440
   macro avg       0.95      0.94      0.93       440
weighted avg       0.95      0.93      0.93       440


# Cross validation score (KNN)
score = cross_val_score(knn,features,target,cv=5)
score

array([0.92727273, 0.95454545, 0.93863636, 0.91590909, 0.91590909])


# Dump the trained KNN classifier with Pickle
knn_pkl_filename = 'knn.pkl'
# Open the file to save as pkl file
knn_Model_pkl = open(knn_pkl_filename, 'wb')
pickle.dump(knn, knn_Model_pkl)
# Close the pickle instances
knn_Model_pkl.close()


cm = confusion_matrix(Ytest, predicted_values)

plt.figure(figsize=(15,15))
sns.heatmap(cm, annot=True, fmt=".0f", linewidths=.5, square = True, cmap = 'Blues');
plt.ylabel('Actual label');
plt.xlabel('Predicted label');
all_sample_title = 'Confusion Matrix - score:'+str(accuracy_score(Ytest,predicted_values))
plt.title(all_sample_title, size = 15);
plt.show()


data = pd.DataFrame([[83, 45, 60, 28, 70.3, 7.0]], columns=['N', 'P','K','temperature', 'humidity', 'ph'])
prediction = knn.predict(data)
print(prediction)

['jute']


import xgboost as xgb
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()

Ytrain = label_encoder.fit_transform(Ytrain)
Ytest = label_encoder.fit_transform(Ytest)
target = label_encoder.fit_transform(target)

XB = xgb.XGBClassifier()

param_grid = {
    'learning_rate': [0.055],
    'max_depth': [3],
    'n_estimators': [150],
    'subsample': [ 1.0],
    'colsample_bytree': [1.0],
    'reg_alpha': [0.1],
    'reg_lambda': [0.1],
    'min_child_weight': [1]
}

grid_search = GridSearchCV(estimator=XB, param_grid=param_grid, cv=5)

grid_search.fit(Xtrain,Ytrain)

best_model = grid_search.best_estimator_

predicted_values = best_model.predict(Xtest)


x = metrics.accuracy_score(Ytest, predicted_values)
acc.append(x)
model.append('XGBoost')
print("XGBoost's Accuracy is: ", x)

XGBoost's Accuracy is:  0.9704545454545455


# Cross validation score (XGBoost)
score = cross_val_score(best_model,features,target,cv=5)
score

array([0.96818182, 0.96363636, 0.96363636, 0.96363636, 0.96590909])


cm = confusion_matrix(Ytest, predicted_values)

plt.figure(figsize=(15,15))
sns.heatmap(cm, annot=True, fmt=".0f", linewidths=.5, square = True, cmap = 'Blues');
plt.ylabel('Actual label');
plt.xlabel('Predicted label');
all_sample_title = 'Confusion Matrix - score:'+str(accuracy_score(Ytest,predicted_values))
plt.title(all_sample_title, size = 15);
plt.show()


from sklearn.preprocessing import LabelEncoder
data = pd.DataFrame([[85,58,41,21.770462,80.319644,7.038096]], columns=['N', 'P','K','temperature', 'humidity', 'ph'])
prediction = best_model.predict(data)
print(prediction)

[20]


data = pd.DataFrame([[83, 55, 65, 28, 70.3, 7.0]], columns=['N', 'P','K','temperature', 'humidity', 'ph'])
prediction = best_model.predict(data)
print(prediction)

[5]

	N	P	K	temperature	humidity	ph	rainfall
count	2200.000000	2200.000000	2200.000000	2200.000000	2200.000000	2200.000000	2200.000000
mean	50.551818	53.362727	48.149091	25.616244	71.481779	6.469480	103.463655
std	36.917334	32.985883	50.647931	5.063749	22.263812	0.773938	54.958389
min	0.000000	5.000000	5.000000	8.825675	14.258040	3.504752	20.211267
25%	21.000000	28.000000	20.000000	22.769375	60.261953	5.971693	64.551686
50%	37.000000	51.000000	32.000000	25.598693	80.473146	6.425045	94.867624
75%	84.250000	68.000000	49.000000	28.561654	89.948771	6.923643	124.267508
max	140.000000	145.000000	205.000000	43.675493	99.981876	9.935091	298.560117

	N	P	K	temperature	humidity	ph	rainfall	label
2195	107	34	32	26.774637	66.413269	6.780064	177.774507	coffee
2196	99	15	27	27.417112	56.636362	6.086922	127.924610	coffee
2197	118	33	30	24.131797	67.225123	6.362608	173.322839	coffee
2198	117	32	34	26.272418	52.127394	6.758793	127.175293	coffee
2199	104	18	30	23.603016	60.396475	6.779833	140.937041	coffee

	N	P	K	temperature	humidity	ph	rainfall	label
1100	2	40	27	29.737700	47.548852	5.954627	90.095869	mango
1101	39	24	31	33.556956	53.729798	4.757115	98.675276	mango
1102	21	26	27	27.003155	47.675254	5.699587	95.851183	mango
1103	25	22	25	33.561502	45.535566	5.977414	95.705259	mango
1104	0	21	32	35.898556	54.259642	6.430139	92.197217	mango
...	...	...	...	...	...	...	...	...
1195	19	38	26	31.484517	48.779263	4.525722	93.172220	mango
1196	21	21	30	27.698193	51.415932	5.403908	100.772070	mango
1197	22	18	33	30.412358	52.481006	6.621624	93.923759	mango
1198	31	20	30	32.177520	54.013527	6.207496	91.887661	mango
1199	18	26	31	32.611261	47.749165	5.418475	91.101908	mango

	N	P	K	temperature	humidity	ph	rainfall	label
1300	119	25	51	26.473302	80.922544	6.283818	53.657426	watermelon
1301	119	19	55	25.187800	83.446217	6.818261	46.874209	watermelon
1302	105	30	50	25.299547	81.775276	6.376201	57.041471	watermelon
1303	114	8	50	24.746313	88.308663	6.581588	57.958261	watermelon
1304	93	22	52	26.587407	81.325632	6.932740	41.875400	watermelon
...	...	...	...	...	...	...	...	...
1395	97	12	47	25.287846	89.636679	6.765095	58.286977	watermelon
1396	110	7	45	26.638386	84.695469	6.189214	48.324286	watermelon
1397	96	18	50	25.331045	84.305338	6.904242	41.532187	watermelon
1398	83	23	55	26.897502	83.892415	6.463271	43.971937	watermelon
1399	120	24	47	26.986037	89.413849	6.260839	58.548767	watermelon

	N	P	K	temperature	humidity	ph	rainfall	label
500	3	49	18	27.910952	64.709306	3.692864	32.678919	mothbeans
521	22	49	22	28.234947	61.562052	3.711059	72.666664	mothbeans
526	8	60	18	31.216300	46.018682	3.808429	53.120528	mothbeans
529	36	43	24	27.094006	43.653054	3.510404	41.537495	mothbeans
535	11	45	19	28.700121	44.359648	3.828031	44.116221	mothbeans
537	17	57	20	28.506779	45.200945	3.793575	66.176146	mothbeans
557	4	46	15	31.012749	62.403925	3.504752	63.771924	mothbeans
561	35	51	17	28.799292	49.842134	3.558823	40.855347	mothbeans
582	19	51	25	26.804744	48.239914	3.525366	43.878020	mothbeans
599	16	51	21	31.019636	49.976752	3.532009	32.812965	mothbeans

Describing the data¶

Viewing and Selecting¶

As rainfall in hydroponic condition is not our concern.¶

Splitting into train and test data¶

Decision Tree¶

Support Vector Machine¶

Random Forest¶

KNN¶

XG Boost¶

	N	P	K	temperature	humidity	ph	rainfall	label
0	90	42	43	20.879744	82.002744	6.502985	202.935536	rice
1	85	58	41	21.770462	80.319644	7.038096	226.655537	rice
2	60	55	44	23.004459	82.320763	7.840207	263.964248	rice
3	74	35	40	26.491096	80.158363	6.980401	242.864034	rice
4	78	42	42	20.130175	81.604873	7.628473	262.717340	rice
...	...	...	...	...	...	...	...	...
2195	107	34	32	26.774637	66.413269	6.780064	177.774507	coffee
2196	99	15	27	27.417112	56.636362	6.086922	127.924610	coffee
2197	118	33	30	24.131797	67.225123	6.362608	173.322839	coffee
2198	117	32	34	26.272418	52.127394	6.758793	127.175293	coffee
2199	104	18	30	23.603016	60.396475	6.779833	140.937041	coffee

label	apple	banana	blackgram	chickpea	coconut	coffee	cotton	grapes	jute	kidneybeans	...	mango	mothbeans	mungbean	muskmelon	orange	papaya	pigeonpeas	pomegranate	rice	watermelon
N
0	3	0	0	0	4	0	0	1	0	2	...	3	1	1	0	4	0	1	3	0	0
1	2	0	0	0	3	0	0	1	0	1	...	3	0	2	0	3	0	3	1	0	0
2	6	0	0	0	2	0	0	1	0	1	...	4	2	3	0	0	0	1	2	0	0
3	1	0	0	0	2	0	0	2	0	2	...	2	3	0	0	0	0	3	3	0	0
4	0	0	0	0	0	0	0	2	0	1	...	2	4	5	0	3	0	1	6	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
134	0	0	0	0	0	0	2	0	0	0	...	0	0	0	0	0	0	0	0	0	0
135	0	0	0	0	0	0	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0
136	0	0	0	0	0	0	2	0	0	0	...	0	0	0	0	0	0	0	0	0	0
139	0	0	0	0	0	0	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0
140	0	0	0	0	0	0	3	0	0	0	...	0	0	0	0	0	0	0	0	0	0

	N	P	K	temperature	humidity	ph	rainfall
label
apple	20.80	134.22	199.89	22.630942	92.333383	5.929663	112.654779
banana	100.23	82.01	50.05	27.376798	80.358123	5.983893	104.626980
blackgram	40.02	67.47	19.24	29.973340	65.118426	7.133952	67.884151
chickpea	40.09	67.79	79.92	18.872847	16.860439	7.336957	80.058977
coconut	21.98	16.93	30.59	27.409892	94.844272	5.976562	175.686646
coffee	101.20	28.74	29.94	25.540477	58.869846	6.790308	158.066295
cotton	117.77	46.24	19.56	23.988958	79.843474	6.912675	80.398043
grapes	23.18	132.53	200.11	23.849575	81.875228	6.025937	69.611829
jute	78.40	46.86	39.99	24.958376	79.639864	6.732778	174.792798
kidneybeans	20.75	67.54	20.05	20.115085	21.605357	5.749411	105.919778
lentil	18.77	68.36	19.41	24.509052	64.804785	6.927932	45.680454
maize	77.76	48.44	19.79	22.389204	65.092249	6.245190	84.766988
mango	20.07	27.18	29.92	31.208770	50.156573	5.766373	94.704515
mothbeans	21.44	48.01	20.23	28.194920	53.160418	6.831174	51.198487
mungbean	20.99	47.28	19.87	28.525775	85.499975	6.723957	48.403601
muskmelon	100.32	17.72	50.08	28.663066	92.342802	6.358805	24.689952
orange	19.58	16.55	10.01	22.765725	92.170209	7.016957	110.474969
papaya	49.88	59.05	50.04	33.723859	92.403388	6.741442	142.627839
pigeonpeas	20.73	67.73	20.29	27.741762	48.061633	5.794175	149.457564
pomegranate	18.87	18.75	40.21	21.837842	90.125504	6.429172	107.528442
rice	79.89	47.58	39.87	23.689332	82.272822	6.425471	236.181114
watermelon	99.42	17.00	50.22	25.591767	85.160375	6.495778	50.786219

	N	P	K	temperature	humidity	ph	rainfall	label
0	False	False	False	False	False	False	False	False
1	False	False	False	False	False	False	False	False
2	False	False	False	False	False	False	False	False
3	False	False	False	False	False	False	False	False
4	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...
2195	False	False	False	False	False	False	False	False
2196	False	False	False	False	False	False	False	False
2197	False	False	False	False	False	False	False	False
2198	False	False	False	False	False	False	False	False
2199	False	False	False	False	False	False	False	False

	N	P	K	temperature	humidity	ph	rainfall	label
1453	93	22	48	29.125337	91.522911	6.776988	21.904404	muskmelon
2143	113	33	34	26.003740	62.144510	6.559817	153.477776	coffee
726	49	68	22	28.568406	61.532786	7.127064	63.497263	blackgram
236	57	58	77	18.726494	17.584064	7.978997	81.201765	chickpea
1457	100	14	49	29.488830	91.075742	6.365957	26.019094	muskmelon
1896	28	27	32	28.940997	93.001090	5.764615	191.772309	coconut
2181	101	31	26	26.708975	69.711841	6.861235	158.860889	coffee
1505	32	137	204	22.860066	93.128599	5.824152	117.729673	apple
733	53	67	17	31.776817	69.018529	7.296972	61.468929	blackgram
451	24	73	20	19.637362	32.315289	4.608695	176.413409	pigeonpeas

	N	P	K	temperature	humidity	ph	rainfall
N	1.000000	-0.231460	-0.140512	0.026504	0.190688	0.096683	0.059020
P	-0.231460	1.000000	0.736232	-0.127541	-0.118734	-0.138019	-0.063839
K	-0.140512	0.736232	1.000000	-0.160387	0.190859	-0.169503	-0.053461
temperature	0.026504	-0.127541	-0.160387	1.000000	0.205320	-0.017795	-0.030084
humidity	0.190688	-0.118734	0.190859	0.205320	1.000000	-0.008483	0.094423
ph	0.096683	-0.138019	-0.169503	-0.017795	-0.008483	1.000000	-0.109069
rainfall	0.059020	-0.063839	-0.053461	-0.030084	0.094423	-0.109069	1.000000

	N	P	K	temperature	humidity	ph	label
1600	22	30	12	15.781442	92.510777	6.354007	orange
1601	37	6	13	26.030973	91.508193	7.511755	orange
1602	27	13	6	13.360506	91.356082	7.335158	orange
1603	7	16	9	18.879577	92.043045	7.813917	orange
1604	20	7	9	29.477417	91.578029	7.129137	orange

label	apple	banana	blackgram	chickpea	coconut	coffee	cotton	grapes	jute	kidneybeans	...	mango	mothbeans	mungbean	muskmelon	orange	papaya	pigeonpeas	pomegranate	rice	watermelon
N
0	3	0	0	0	4	0	0	1	0	2	...	3	1	1	0	4	0	1	3	0	0
1	2	0	0	0	3	0	0	1	0	1	...	3	0	2	0	3	0	3	1	0	0
2	6	0	0	0	2	0	0	1	0	1	...	4	2	3	0	0	0	1	2	0	0
3	1	0	0	0	2	0	0	2	0	2	...	2	3	0	0	0	0	3	3	0	0
4	0	0	0	0	0	0	0	2	0	1	...	2	4	5	0	3	0	1	6	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
134	0	0	0	0	0	0	2	0	0	0	...	0	0	0	0	0	0	0	0	0	0
135	0	0	0	0	0	0	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0
136	0	0	0	0	0	0	2	0	0	0	...	0	0	0	0	0	0	0	0	0	0
139	0	0	0	0	0	0	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0
140	0	0	0	0	0	0	3	0	0	0	...	0	0	0	0	0	0	0	0	0	0

label	apple	banana	blackgram	chickpea	coconut	coffee	cotton	grapes	jute	kidneybeans	...	mango	mothbeans	mungbean	muskmelon	orange	papaya	pigeonpeas	pomegranate	rice	watermelon
N
0	3	0	0	0	4	0	0	1	0	2	...	3	1	1	0	4	0	1	3	0	0
1	2	0	0	0	3	0	0	1	0	1	...	3	0	2	0	3	0	3	1	0	0
2	6	0	0	0	2	0	0	1	0	1	...	4	2	3	0	0	0	1	2	0	0
3	1	0	0	0	2	0	0	2	0	2	...	2	3	0	0	0	0	3	3	0	0
4	0	0	0	0	0	0	0	2	0	1	...	2	4	5	0	3	0	1	6	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
134	0	0	0	0	0	0	2	0	0	0	...	0	0	0	0	0	0	0	0	0	0
135	0	0	0	0	0	0	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0
136	0	0	0	0	0	0	2	0	0	0	...	0	0	0	0	0	0	0	0	0	0
139	0	0	0	0	0	0	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0
140	0	0	0	0	0	0	3	0	0	0	...	0	0	0	0	0	0	0	0	0	0